Analytical Formulation of the Performance of the Allam Power Cycle

Thermal power plants operating on fossil fuels emit a considerable amount of polluting gases including carbon dioxide and nitrogen oxides. Several technologies have been developed or under development to avoid the emissions of, mainly, CO2 that are formed as a result of air-fuel combustion. While post-combustion capture methods are viable solutions for reduction of CO2 in the existing power plants, implementation of the concept of oxyfuel combustion in future power cycles appears to be a promising technique for clean power generation from fossil fuels. A novel power cycle that employs oxyfuel combustion method has been developed by NET Power. Known as the Allam cycle, it includes a turbine, an air separation unit (ASU), a combustor, a recuperator, a water separator, CO2 compression with intercooling and CO2 pump. (Over 90% of the supercritical CO2 flow is recycled back to the cycle as the working fluid, and the rest is extracted for further processing and storage. The present paper introduces a simplified thermodynamic analysis of the Allam power cycle. Analytical expressions are derived for the net power output, optimum turbine inlet temperature (TIT), and the molar flowrate of the recycled CO2 flow. The study aims to provide a theoretical framework to help understand the functional relationships between the various operating parameters of the cycle. The optimum TIT predicted by the presented expression is 1473 K which is fairly close to that reported by the cycle developers.

Combustion Tuning for a Gas Turbine Power Plant Using Data-Driven and Machine Learning Approach

In this paper, we present a cross-disciplinary study on the combustion tuning of an F-class gas turbine that combines machine learning with physics understanding. An artificial-neural-network-based (ANN) model is developed to predict the combustion performance (outputs), including NOx emissions, combustion dynamics, combustor acceleration, and turbine exhaust temperature. The inputs of the ANN model are identified by analyzing the key operating variables that impact the combustion performance, such as the pilot and the premixed fuel flow, and the inlet guide vane angle. The ANN model is trained by field data from an F-class gas turbine power plant. The trained model is able to describe the combustion performance at an acceptable accuracy in a wide range of operating conditions. In combination with the genetic algorithm, the model is applied to optimize the combustion performance of the gas turbine. Results demonstrate that the data-driven method offers a promising alternative for combustion tuning at a low cost and fast turn-around.

Assessment of Current Capabilities and Near-Term Availability of Hydrogen-Fired Gas Turbines Considering a Low-Carbon Future

A confluence of technology development, policy support, and industry investment trends are accelerating the pace of Hydrogen (H2) technology demonstrations, increasing the likelihood of power sector impacts. In preparation for a large-scale power sector shift toward decarbonization for a low-carbon future, several major power equipment manufacturers are developing gas turbines that can operate on a high H2-volume fuel. Many have H2 capable systems now that range from 5 to 100% H2. Units with 100% H2 capabilities are either using a diffusion burner or some version of a wet low emissions (WLE) burner. Most dry low emission/dry low NOx (DLE/DLN) technologies are currently limited to ∼60% H2 or less. Therefore, research is currently underway to develop low NOx gas turbine combustion systems with improved Hydrogen capability. This paper provides an overview of the technical challenges of Hydrogen combustion and the probable technologies with which the manufacturers will respond.

Development of Web-Based Short Courses on Control, Diagnostics, and Instrumentation

As a consequence of globalization and advances in digital tools, synchronous or asynchronous distance courses are becoming an integral part of universities’ educational offers. The design of an online course introduces more challenges compared to a traditional on campus course with face to face lectures. This is true especially for engineering subjects where problem or project-based courses may be preferred to stimulate critical thinking and engage the learners with real-life problems. However, realizing this with distance learning implies that a similar study pace should be kept by the learners involved. This may not be easy, since individual pace is often a motivation for choosing a distance course. Student engagement in group projects, collaborations, and the proper design of examination tasks are only some of the challenges in designing a distance course for an engineering program. A series of web-based courses on measurement techniques, control, and diagnostics were developed and delivered to groups of learners. Each course comprised short modules covering key points of the subject and aimed at getting learners to understand both the fundamental concepts that they do not typically learn or understand in the respective base courses and to build on that knowledge to reach a more advanced cognitive level. The experience obtained in the courses on what strategies worked better or worse for the learners is presented in this paper. A comparison between the courses provides an interesting outlook on how the learners reacted to slightly different requirements and incentives in each course. The results from the evaluation of the courses are also used as a base for discussion. The background and availability of the learners is closely linked to how a course should be designed to optimally fit the learning group, without compromising on the achievement of the learning outcomes. This series of courses is a good example of continuous professional development courses in the field of control, diagnostics, and instrumentation (CDI), and brings with it a number of challenges and opportunities for the development of online courses.

A Review of the Prospect and Challenges for Developing and Marketing a Brayton-Cycle Based Power Genset Gas-Turbine Using Supercritical CO2: Part I — The General System Challenges

Around the 1960s, the proposal of the supercritical dioxide (s-CCO2) power-cycle was first introduced; however, because of various obstacles, the development was slow at that time. With current worldwide emission and power problems, the s-CO2 power cycle has regained more attention because of its unique properties as a working fluid for power-cycle, and zero-emission potential. Each s-CO2 power cycle requires various components for compression, expansion, and heat exchange operation. Among various working fluid, s-CO2 has four significant advantages favorable for developers: 1. Relatively low and achievable critical conditions (∼7.3Mpa, ∼31°C). 2. The high density (∼400kg/m3) results in a very compact turbomachinery design. 3. The low dynamic-viscosity of s-CO2 can reduce the overall flow friction loss. 4. Low compressibility value which can reduce the overall system compression works. All these advantages make the s-CO2 the perfect working fluid for next-generation high-efficiency power-cycle design. This paper in two parts reviews the s-CO2 cycle technologies for power generation and critically assesses the recent challenges and development status. This paper, Part I, focuses on the general cycle concepts, thermodynamic properties, materials selection, and other components considerations.

Introduction to Multi-Point Design Strategies for Aero Engines

Classic gas turbine design relies on the definition of a design point, and the subsequent assessment of the design on a range of off-design conditions. On the design point, both component sizing (e.g., in terms of physical dimensions or in terms of map scaling parameters) and a solution to the off-design governing equations are established. With this approach, it is however difficult to capture the contradicting requirements on the full operating envelope. Thus, practical design efforts rely on various multi-point design approaches. This paper introduces a simplified notation of such multi-point approaches via synthesis matching tables. It then summarizes two academic state-of-the-art multi-point design schemes using such tables in a comprehensible fashion. The target audience are students and engineers familiar with the basics of classic cycle design and analysis looking for a practical introduction to such multi-point design approaches. Application examples are given in terms of a simple turbojet and a typical geared turbofan as modeled in state-of-the-art academic cycle design and analysis efforts. The results of the classic design point approach are compared to those of multi-point approaches.

Improving the Safety of Power Plants by Developing a Digital Twin and an Expert System for Adaptive-Predictive Analysis of the Operability of Gas Turbine Units

The article describes general approaches to creating an intelligent system for monitoring and diagnosing the operability of energy supply facilities. The general concept of the adaptive-predictive analysis system and the construction of an artificial neural network for its use in the predictive module for predicting the type and time of failure occurrence is given. The basic principles of training a neural network for recognizing various types of failures are also given. Critical remarks of the concept of creating a digital twin of such a complex object for modeling as energy-generating equipment are given.

A Toolbox of Hardware and Digital Solutions for Increased Flexibility

The power generation market has been changing rapidly with the injection of an ever increasing usage of renewable power sources. The cyclic and highly unpredictable nature of power generation output from renewable sources is forcing Gas Turbine (GT) operators to significantly increase the operational flexibility of their engines. While the industry has been, for many years, developing and fielding solutions providing increased output at the high end of the operating range, the focus has shifted recently to solutions allowing for a safe decrease of the engines’ minimum operating load. The AutoTune (AT) system was introduced at last year’s Turbo Expo conference [5], and the challenges of developing a safe Extended Turndown add-on are detailed herein. Other digital and hardware solutions presented include Part Load Performance, decreased start-up time for both simple and combined cycle units, disc cavity cooling modulation and Exhaust Bleed. Increased ramp rate is addressed with the associated significant difficulty of maintaining the mechanical integrity of the rotors and casings. PSM has been working on a toolbox of both hardware and digital solutions to increase on GT operability both on the high and low ends of the load range and the technical issues faced are described in this paper.

Balancing a High-Renewables Electric Grid With Hydrogen-Fuelled Combined Cycles: A Country Scale Analysis

A key approach to large renewable energy sources (RES) power management is based on implementing storage technologies, including batteries, power-to-hydrogen (P2H), pumped-hydro, and compressed air energy storage. Power-to-hydrogen presents specific advantages in terms of suitability for large-scale and long-term energy storage as well as capability to decarbonize a wide range of end-use sectors, e.g., including both power generation and mobility. This work applies a multi-nodal model for the hourly simulation of the energy system at a nation scale, integrating the power, transport, and natural gas sectors. Three main infrastructures are considered: (i) the power grid, characterized by instantaneous supply-demand balance and featuring a variety of storage options; (ii) the natural gas network, which can host a variable hydrogen content, supplying NG-H2 blends to the final consumers; (iii) the hydrogen production, storage, and re-electrification facilities. The aim of the work is to assess the role that can be played by gas turbine-based combined cycles in the future high-RES electric grid. Combined cycles (GTCCs) would exploit hydrogen generated by P2H implementation at large scale, transported through the natural gas infrastructure at increasingly admixed fractions, thus closing the power-to-power (P2P) conversion of excess renewables and becoming a strategic asset for future grid balancing applications. A long-term scenario of the Italian energy system is analyzed, involving a massive increase of intermittent RES power generation capacity and a significant introduction of low-emission vehicles based on electric drivetrains (pure-battery or fuel-cell). The analysis highlights the role of hydrogen as clean energy vector, not only for specific use in new applications like fuel cell vehicles and stationary fuel cells, but also for substitution of fossil fuels in conventional combustion devices. The study also explores the option of repowering the combined cycles at current sites and evaluates the effect of inter-zonal limits on power and hydrogen exchange. Moreover, results include the evaluation of the required hydrogen storage size, distributed at regional scale or in correspondence of the power plant sites. Results show that when extra hydrogen generated by P2H is fed to GTCCs, up to 17–24% H2 use is achieved, reaching up to 70–100% in southern regions, with a parallel reduction in fossil NG input and CO2 emissions of the GTCC plants.

An Ensemble of Recurrent Neural Networks for Real Time Performance Modelling of Three-Spool Aero-Derivative Gas Turbine Engine

Gas turbine is a complex system operating in non-stationary operation conditions for which traditional model-based modelling approaches have poor generalization capabilities. To address this, an investigation of a novel data driven neural networks based model approach for a three-spool aero-derivative gas turbine engine (ADGTE) for power generation during its loading and unloading conditions is reported in this paper. For this purpose, a non-linear autoregressive network with exogenous inputs (NARX) is used to develop this model in MATLAB environment using operational closed-loop data collected from Siemens (SGT-A65) ADGTE. Inspired by the way biological neural networks process information and by their structure which changes depending on their function, multiple-input single-output (MISO) NARX models with different configurations were used to represent each of the ADGTE output parameters with the same input parameters. First, data preprocessing and estimation of the order of these MISO models were performed. Next, a computer program code was developed to perform a comparative study and to select the best NARX model configuration, which can represent the system dynamics. Usage of a single neural network to represent each of the system output parameters may not be able to provide an accurate prediction for unseen data and as a consequence, provides poor generalization. To overcome this problem, an ensemble of MISO NARX models is used to represent each output parameter. The major challenge of the ensemble generation is to decide how to combine results produced by the ensemble’s components. In this paper, a novel hybrid dynamic weighting method (HDWM) is proposed. The verification of this method was performed by comparing its performance with three of the most popular basic methods for ensemble integration: basic ensemble method (BEM), median rule and dynamic weighting method (DWM). Finally, the generated ensembles of MISO NARX models for each output parameter were evaluated using unseen data (testing data). The simulation results based on datasets consisting for experimental data as well as data provided by Siemens high fidelity thermodynamic transient simulation program show improvement in accuracy and robustness by using the proposed modelling approach.